Secure and robust multi-constrained QoS aware routing algorithm for VANETs

Secure QoS routing algorithms are a fundamental part of wireless networks that aim to provide services with QoS and security guarantees. In Vehicular Ad hoc Networks (VANETs), vehicles perform routing functions, and at the same time act as end-systems thus routing control messages are transmitted unprotected over wireless channels. The QoS of the entire network could be degraded by an attack on the routing process, and manipulation of the routing control messages. In this paper, we propose a novel secure and reliable multi-constrained QoS aware routing algorithm for VANETs. We employ the Ant Colony Optimisation (ACO) technique to compute feasible routes in VANETs subject to multiple QoS constraints determined by the data traffic type. Moreover, we extend the VANET-oriented Evolving Graph (VoEG) model to perform plausibility checks on the exchanged routing control messages among vehicles. Simulation results show that the QoS can be guaranteed while applying security mechanisms to ensure a reliable and robust routing service

Adoption of vehicular ad hoc networking protocols by networked robots

This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

Emulating opportunistic networks with KauNet Triggers

In opportunistic networks the availability of an end-to-end path is no longer required. Instead opportunistic networks may take advantage of temporary connectivity opportunities. Opportunistic networks present a demanding environment for network emulation as the traditional emulation setup, where application/transport endpoints only send and receive packets from the network following a black box approach, is no longer applicable. Opportunistic networking protocols and applications additionally need to react to the dynamics of the underlying network beyond what is conveyed through the exchange of packets. In order to support IP-level emulation evaluations of applications and protocols that react to lower layer events, we have proposed the use of emulation triggers. Emulation triggers can emulate arbitrary cross-layer feedback and can be synchronized with other emulation effects. After introducing the design and implementation of triggers in the KauNet emulator, we describe the integration of triggers with the DTN2 reference implementation and illustrate how the functionality can be used to emulate a classical DTN data-mule scenario

Mobile data and computation offloading in mobile cloud computing

Le trafic mobile augmente considérablement en raison de la popularité des appareils mobiles et des applications mobiles. Le déchargement de données mobiles est une solution permettant de réduire la congestion du réseau cellulaire. Le déchargement de calcul mobile peut déplacer les tâches de calcul d'appareils mobiles vers le cloud. Dans cette thèse, nous étudions d'abord le problème du déchargement de données mobiles dans l'architecture du cloud computing mobile. Afin de minimiser les coûts de transmission des données, nous formulons le processus de déchargement des données sous la forme d'un processus de décision de Markov à horizon fini. Nous proposons deux algorithmes de déchargement des données pour un coût minimal. Ensuite, nous considérons un marché sur lequel un opérateur de réseau mobile peut vendre de la bande passante à des utilisateurs mobiles. Nous formulons ce problème sous la forme d'une enchère comportant plusieurs éléments afin de maximiser les bénéfices de l'opérateur de réseau mobile. Nous proposons un algorithme d'optimisation robuste et deux algorithmes itératifs pour résoudre ce problème. Enfin, nous nous concentrons sur les problèmes d'équilibrage de charge afin de minimiser la latence du déchargement des calculs. Nous formulons ce problème comme un jeu de population. Nous proposons deux algorithmes d'équilibrage de la charge de travail basés sur la dynamique évolutive et des protocoles de révision. Les résultats de la simulation montrent l'efficacité et la robustesse des méthodes proposées.Global mobile traffic is increasing dramatically due to the popularity of smart mobile devices and data hungry mobile applications. Mobile data offloading is considered as a promising solution to alleviate congestion in cellular network. Mobile computation offloading can move computation intensive tasks and large data storage from mobile devices to cloud. In this thesis, we first study mobile data offloading problem under the architecture of mobile cloud computing. In order to minimize the overall cost for data delivery, we formulate the data offloading process, as a finite horizon Markov decision process, and we propose two data offloading algorithms to achieve minimal communication cost. Then, we consider a mobile data offloading market where mobile network operator can sell bandwidth to mobile users. We formulate this problem as a multi-item auction in order to maximize the profit of mobile network operator. We propose one robust optimization algorithm and two iterative algorithms to solve this problem. Finally, we investigate computation offloading problem in mobile edge computing. We focus on workload balancing problems to minimize the transmission latency and computation latency of computation offloading. We formulate this problem as a population game, in order to analyze the aggregate offloading decisions, and we propose two workload balancing algorithms based on evolutionary dynamics and revision protocols. Simulation results show the efficiency and robustness of our proposed methods